Ordering sparse multivariate functional data is necessary before we visualize them. We consider a two-step procedure: first we apply multivariate functional principal component analysis (MFPCA, [1]} [1]}) to fit the data; second, we consider various notions of depth to order the fitted data. If there is only one variable, then the data reduce to univariate functional data, and MFPCA becomes univariate functional principal component analysis (UFPCA, [3]} [3]}). The depth notions we consider are either from the direct generalization of the univariate revised functional depth ([5]} [5]}) to multivariate functional data, or our new idea described in Section REF .
